from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

Time report

print_time_report()

	hour	min	sec
algo
KNeighborsClassifier	0.0	34.0	45.945464
daal4py_KNeighborsClassifier	0.0	6.0	20.389947
KNeighborsClassifier_kd_tree	0.0	2.0	41.542006
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	33.014717
KMeans_tall	0.0	0.0	27.468542
daal4py_KMeans_tall	0.0	0.0	11.051374
KMeans_short	0.0	0.0	3.810694
daal4py_KMeans_short	0.0	0.0	1.912009
LogisticRegression	0.0	0.0	26.086144
daal4py_LogisticRegression	0.0	0.0	5.944616
Ridge	0.0	0.0	11.442383
daal4py_Ridge	0.0	0.0	2.498267
HistGradientBoostingClassifier	0.0	6.0	35.438816
lightgbm	0.0	5.0	19.880914
xgboost	0.0	5.0	1.702277
catboost	0.0	5.0	7.042090
total	1.0	7.0	55.275298

Environment information

print_env_info()

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

scikit-learn's estimators vs daal4py

reporting = Reporting(config_file_path="config.yml")
reporting.run()

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.156	0.000	5.132	0.000	-1	100	NaN	NaN	0.593	0.000	0.263	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	40.718	0.000	0.000	0.041	-1	100	0.934	0.724	4.508	0.066	9.032	0.132	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.208	0.016	0.000	0.208	-1	100	1.000	1.000	0.110	0.003	1.902	0.161	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.161	0.000	4.972	0.000	1	1	NaN	NaN	0.567	0.000	0.284	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	17.658	0.137	0.000	0.018	1	1	0.705	0.813	4.531	0.071	3.897	0.068	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.222	0.008	0.000	0.222	1	1	1.000	1.000	0.105	0.004	2.118	0.107	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.144	0.000	5.546	0.000	1	5	NaN	NaN	0.516	0.000	0.279	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	27.349	0.264	0.000	0.027	1	5	0.808	0.813	4.464	0.089	6.127	0.136	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.236	0.007	0.000	0.236	1	5	1.000	1.000	0.109	0.004	2.165	0.096	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.139	0.000	5.764	0.000	-1	5	NaN	NaN	0.555	0.000	0.250	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	42.083	0.000	0.000	0.042	-1	5	0.808	0.944	4.543	0.087	9.264	0.177	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.201	0.017	0.000	0.201	-1	5	1.000	1.000	0.106	0.006	1.895	0.195	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.134	0.000	5.961	0.000	-1	1	NaN	NaN	0.552	0.000	0.243	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	32.922	0.000	0.000	0.033	-1	1	0.705	0.724	4.462	0.088	7.379	0.146	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.202	0.014	0.000	0.202	-1	1	1.000	1.000	0.103	0.007	1.955	0.190	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.155	0.000	5.158	0.000	1	100	NaN	NaN	0.535	0.000	0.290	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	27.086	0.800	0.000	0.027	1	100	0.934	0.944	4.565	0.071	5.933	0.198	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.232	0.004	0.000	0.232	1	100	1.000	1.000	0.112	0.009	2.084	0.168	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.064	0.000	0.250	0.000	-1	100	NaN	NaN	0.089	0.000	0.720	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	35.767	0.000	0.000	0.036	-1	100	0.982	0.981	0.975	0.019	36.684	0.719	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.035	0.005	0.000	0.035	-1	100	1.000	1.000	0.005	0.000	7.731	1.224	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.283	0.000	1	1	NaN	NaN	0.099	0.000	0.569	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	11.658	0.114	0.000	0.012	1	1	0.975	0.988	1.001	0.018	11.652	0.240	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.005	0.001	2.919	0.678	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.051	0.000	0.312	0.000	1	5	NaN	NaN	0.099	0.000	0.516	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.055	0.291	0.000	0.021	1	5	0.982	0.988	1.014	0.015	20.769	0.418	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.024	0.001	0.000	0.024	1	5	1.000	1.000	0.005	0.001	4.845	1.115	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.074	0.000	0.215	0.000	-1	5	NaN	NaN	0.106	0.000	0.702	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	35.052	0.000	0.000	0.035	-1	5	0.982	0.992	1.086	0.017	32.266	0.497	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.002	0.000	0.030	-1	5	1.000	1.000	0.005	0.000	6.138	0.623	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.277	0.000	-1	1	NaN	NaN	0.108	0.000	0.535	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	25.493	0.191	0.000	0.025	-1	1	0.975	0.981	1.017	0.025	25.071	0.649	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.003	0.000	0.021	-1	1	1.000	1.000	0.005	0.000	4.717	0.769	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.293	0.000	1	100	NaN	NaN	0.098	0.000	0.561	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.664	0.883	0.000	0.022	1	100	0.982	0.992	1.071	0.010	20.229	0.846	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.026	0.001	0.000	0.026	1	100	1.000	1.000	0.005	0.001	5.411	0.766	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.901	0.000	0.028	0.000	-1	100	NaN	NaN	0.789	0.000	3.676	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.856	0.066	0.000	0.003	-1	100	0.973	0.970	0.120	0.004	23.799	0.954	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.000	0.000	14.365	10.961	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.962	0.000	0.027	0.000	1	1	NaN	NaN	0.803	0.000	3.689	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.731	0.028	0.000	0.001	1	1	0.957	0.970	0.122	0.006	5.984	0.370	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	2.663	1.544	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.878	0.000	0.028	0.000	1	100	NaN	NaN	0.757	0.000	3.804	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.797	0.132	0.000	0.005	1	100	0.973	0.984	0.224	0.009	21.395	1.013	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.000	0.000	7.049	4.048	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.722	0.000	0.029	0.000	1	5	NaN	NaN	0.785	0.000	3.467	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.449	0.017	0.000	0.001	1	5	0.972	0.984	0.671	0.024	2.160	0.080	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.001	0.000	1.385	0.582	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.846	0.000	0.028	0.000	-1	1	NaN	NaN	0.751	0.000	3.792	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.465	0.011	0.000	0.000	-1	1	0.957	0.984	0.219	0.010	2.130	0.108	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	1	1.000	1.000	0.000	0.000	7.871	4.115	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.804	0.000	0.029	0.000	-1	5	NaN	NaN	0.782	0.000	3.586	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.860	0.022	0.000	0.001	-1	5	0.972	0.984	0.695	0.012	1.237	0.037	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.001	0.000	3.651	1.492	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.771	0.000	0.021	0.000	-1	100	NaN	NaN	0.544	0.000	1.417	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.058	0.003	0.000	0.000	-1	100	0.977	0.979	0.001	0.000	61.282	17.865	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.002	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	18.513	11.501	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.844	0.000	0.019	0.000	1	1	NaN	NaN	0.555	0.000	1.521	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.035	0.002	0.000	0.000	1	1	0.970	0.979	0.001	0.000	40.367	9.985	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.803	2.808	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.789	0.000	0.020	0.000	1	100	NaN	NaN	0.554	0.000	1.424	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.064	0.003	0.000	0.000	1	100	0.977	0.983	0.001	0.000	47.620	10.691	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	4.118	2.455	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.760	0.000	0.021	0.000	1	5	NaN	NaN	0.540	0.000	1.407	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.036	0.003	0.000	0.000	1	5	0.980	0.982	0.008	0.001	4.285	0.540	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.443	2.419	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.824	0.000	0.019	0.000	-1	1	NaN	NaN	0.541	0.000	1.523	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.041	0.004	0.000	0.000	-1	1	0.970	0.983	0.001	0.000	30.505	7.363	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	16.556	7.053	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.777	0.000	0.021	0.000	-1	5	NaN	NaN	0.528	0.000	1.472	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.040	0.001	0.000	0.000	-1	5	0.980	0.982	0.008	0.001	4.841	0.473	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	13.913	8.641	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.701	0.000	0.685	0.000	random	NaN	30	NaN	0.336	0.0	2.084	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.278	0.000	random	0.001	30	0.001	0.000	0.0	7.328	2.313	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.001	0.000	0.002	random	1.000	30	1.000	0.000	0.0	8.836	4.135	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.680	0.000	0.706	0.000	k-means++	NaN	28	NaN	0.325	0.0	2.092	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.276	0.000	k-means++	0.001	28	0.001	0.000	0.0	6.723	2.356	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	k-means++	1.000	28	1.000	0.000	0.0	7.464	3.804	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	8.212	0.000	2.923	0.000	random	NaN	30	NaN	4.107	0.0	1.999	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	12.409	0.000	random	0.001	30	0.002	0.000	0.0	5.807	2.352	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.014	0.002	random	1.000	30	1.000	0.000	0.0	8.152	4.228	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	8.450	0.000	2.840	0.000	k-means++	NaN	30	NaN	4.440	0.0	1.903	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	12.242	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.739	2.483	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.014	0.002	k-means++	1.000	30	1.000	0.000	0.0	7.052	3.869	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.334	0.0	0.010	0.000	k-means++	NaN	20	NaN	0.059	0.0	5.654	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.143	0.000	k-means++	0.000	20	0.002	0.001	0.0	3.014	0.801	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.672	5.534	See	See
3	KMeans_short	fit	10000	10000	2	20	0.109	0.0	0.029	0.000	random	NaN	20	NaN	0.154	0.0	0.705	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.149	0.000	random	-0.001	20	-0.001	0.001	0.0	2.813	0.783	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	random	1.000	20	1.000	0.000	0.0	7.790	4.802	See	See
6	KMeans_short	fit	10000	10000	100	20	1.211	0.0	0.132	0.000	k-means++	NaN	20	NaN	0.279	0.0	4.333	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.004	0.0	4.531	0.000	k-means++	0.329	20	0.333	0.002	0.0	2.060	0.358	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	k-means++	1.000	20	1.000	0.000	0.0	7.246	3.965	See	See
9	KMeans_short	fit	10000	10000	100	20	0.372	0.0	0.430	0.000	random	NaN	20	NaN	0.625	0.0	0.595	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	4.607	0.000	random	0.387	20	0.330	0.002	0.0	1.887	0.519	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	random	1.000	20	1.000	0.000	0.0	7.057	3.964	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	16.233	0.0	[-0.07268544]	0.000	NaN	NaN	NaN	NaN	NaN	3.148	0.0	5.156	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[43.09035402]	0.000	NaN	NaN	NaN	NaN	0.507	0.000	0.0	0.764	0.349	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.15737081]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.343	0.327	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	1.347	0.0	[1.54471968]	0.001	NaN	NaN	NaN	NaN	NaN	1.127	0.0	1.195	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.003	0.0	[79.93637679]	0.000	NaN	NaN	NaN	NaN	0.250	0.004	0.0	0.671	0.096	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[13.57160195]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.174	0.072	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.331	0.000	0.242	0.0	NaN	NaN	NaN	0.329	0.000	1.007	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.012	0.001	6.514	0.0	NaN	NaN	0.124	0.020	0.001	0.627	0.060	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	0.691	0.0	NaN	NaN	NaN	0.000	0.000	0.615	0.541	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.643	0.000	0.487	0.0	NaN	NaN	NaN	0.437	0.000	3.757	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	2.810	0.0	NaN	NaN	1.000	0.000	0.000	1.010	0.620	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.008	0.0	NaN	NaN	NaN	0.000	0.000	0.662	0.512	See	See

Randomized HPO Benchmarks

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()

Scatter

Permutated curves

25th percentile

Mean

75th percentile